.png){kind=icon}
.png){kind=icon}
Return
Optimal control strategies integrating higher-order diffusion-guided local intervention with sparse control for infectious disease mitigation
.png){kind=icon}
PRE
AI
R
X
G
DOI：10.1007/s11071-026-12936-4.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Higher-order cooperative mobility can substantially reshape the spatial diffusion of infectious diseases, but its role in resource-limited epidemic control remains insufficiently understood. This paper develops a higher-order diffusion-guided sparse optimal control framework for networked epidemic reaction–diffusion systems. We first analyze the emergence of Turing patterns under higher-order nonlinear diffusion and derive the corresponding instability conditions. Based on these pattern-forming mechanisms, we then formulate an optimal control problem that integrates higher-order diffusion-guided local intervention with sparse recovery-related control. The proposed framework embeds structural information from higher-order diffusion into the sparse control process, enabling intervention resources to be allocated to dynamically and structurally relevant nodes. To identify the best strategy among multiple feasible control schemes, we introduce a comprehensive performance index that balances the relative error and the control cost. Numerical results show that the proposed strategy can maintain effective epidemic suppression while reducing redundant intervention costs compared with conventional sparse control. Robustness tests across different initial patterns further confirm the stability of the method. These results provide a theoretical and computational framework for designing structure-aware and resource-efficient epidemic intervention strategies.
Keywords:
Higher-order diffusion
Sparse optimal control
Local intervention
Turing patterns
Resource allocation
Journal
IF:
6
Papers:
1.4W
Citations:
4.1W
Organization
Cited Papers